Overview
The group develops advanced mathematical methods and algorithms to tackle problems in signal and image processing. In particular we advocate the joint optimization of the sensing and processing layers to achieve imaging and inference results that cannot be achieved otherwise. We handle large amount of high-dimensional data and develop model-based as well as learning-based methods.
Recent Talks
- Deep Dictionary Learning Approaches for Image Super-Resolution Department of Applied Mathematics and Theoretical Physics , Cambridge University, March 2020
- Timing is everything: Sparse sampling based on time-encoding machines, Plenary, SPARS 2019, Toulouse, July 2019
Collaborators
Dr M. Tagliasacchi (academic visitor in my lab), Polytechnic of Milan, Image processing, 2012 - 2012
Professor Yue Lu, Harvard University, 2011
Professor Vivek K Goyal, MIT and Boston University, 2011
Prof. A. Hirabayashi (visitor in my group), Yamaguchi University, Sampling Theory, 2009 - 2010
Prof. Thierry Blu, Chinese University of Hong Kong, 2008 - 2013
Professor Martin Vetterli, Ecole Polytechnique Federale de Lausanne (EPFL), 2003
Guest Lectures
Sparse Sampling: Sensing Brain Activity at Infinite Resolution’, Invited Speaker, CNS 2013 Workshop on Methods of Information Theory in Computational Neuroscience, Paris, France, 2013
Approximate Strang-Fix: Sparse Sampling with any Acquisition Device, Summer Research Institute, EPFL, Lausanne, Switzerland, 2013
Sampling and Reconstruction driven by sparsity models: Theory and Applications, IEICE, Japan, 2012
On the Sampling and Compression of the Plenoptic Function’, Plenary talk,, IEEE Workshop on Multimedia Signal Processing (MMSP), October 2010, Saint Malo, France, 2010
Sparse Sampling: Theory and Applications, Keynote talk,, Workshop on Sparsity and Nonlinear Diffusion for Signal and Image Processing,, International Centre for Mathematical Sciences, Edinburgh, November 2009., 2009
Research Staff
Muammar,H
Visentini Scarzanella,M
Zhang,Y